A Method for Bidding Battery Storage Into Hour-Ahead Energy Markets

Web Published:

Bidding Battery Storage In to Hour-Ahead Energy Markets


Princeton Docket # 14-3012


There is growing interest in the use of grid-level storage to smooth variations in supply that are likely to arise with increased use of wind and solar energy. Energy arbitrage, the process of buying, storing, and selling electricity to exploit variations in electricity spot prices, is becoming an important way of paying for expensive investments into grid level storage. Independent system operators such as the New York Independent System Operator (NYISO) require that battery storage operators place bids into an hour-ahead. The operator has to place these bids without knowing the energy level in the battery at the beginning of the hour, while simultaneously accounting for the value of leftover energy at the end of the hour. The problem is formulated as a dynamic program; however, due to the intractability of the large state space, traditional techniques cannot be used to find the optimal policy within a reasonable time frame.


Researchers in the Department of Operations Research and Financial Engineering at Princeton University have developed a near-optimal Approximate Dynamic Programming (ADP) algorithm that exploits monotonicity of the value function to find a revenue-generating bidding policy. Using optimal benchmarks, the ADP algorithm dramatically outperforms popular engineering heuristics. The researchers propose a distribution-free variant of the ADP algorithm that does not require any knowledge of the distribution of the price process and makes no assumptions regarding a specific real-time price model. They demonstrate that a policy trained on historical real-time price data from the NYISO using this distribution-free approach is indeed effective.



·         Battery arbitrage


·         ADP algorithm dramatically outperforms engineering practice


·         ADP algorithm is used to find a revenue-generating bidding policy


·         No knowledge of the distribution of the price process is required


·         Training of the ADP algorithm can be done with real data


·         Battery arbitrage can be an important part of a portfolio of revenue-generating activities



Bidding into the electricity market can be a complicated process, mainly due to the requirement of balancing supply and demand at each point in the grid. To solve this issue, the Independent System Operators (ISOs) and the Regional Transmission Organizations (RTOs) generally use multi-settlement markets: several tiers of markets covering planning horizons that range from day-ahead to real-time. The idea is that the markets further away from the operating time settle the majority of the generation needed to handle the predicted load, while the markets closer to the operating time correct for the small, yet unpredictable deviations that may be caused by issues like weather, transmission problems, and generation. Settlements in these real-time markets are based on a set of intra-hour prices, typically computed at 5, 10, or 15 minute intervals, depending on the specific market in question. A settlement refers to the financial transaction after a generator clears the market, which refers to being selected to either buy or sell energy from the market. If a generator does not clear the market, it remains idle and no settlement occurs. This situation is referred to as being “out of the market”.


Many ISO's and RTO's, such as the PJM Interconnection, deal with the balancing market primarily through the day-ahead market. PJM's balancing market clears every 5 minutes, but the bids are all placed the previous day. In certain markets, however, it is not only possible to settle in real-time, but market participants can also submit bids each hour, for an hour in the future. Thus, a bid consisting of buy and sell prices can be made at 1pm that will govern the battery between 2pm and 3pm. The process of both bidding and settling in real-time is a characteristic of the New York Independent System Operator (NYISO) real-time market. Other prominent examples of markets that include a real-time bidding aspect include California ISO (CAISO) and Midcontinent ISO (MISO). Due to both the higher volatility of real-time prices versus day-ahead prices and the more interesting sequential nature of the hour-ahead bidding problem (rather than placing bids for all 24 hours at once), the researchers consider in this study the real-time market only. In particular, their goal is to pair battery storage with hour-ahead bidding in the real-time market for profit maximization, a strategy sometimes referred to as energy arbitrage.


It is unlikely that profits from battery arbitrage alone can be sustainable for a company; however, if performed optimally, it can be an important part of a range of profit generating activities. The potential for more efficient and cost-effective technology combined with better control policies can make energy arbitrage feasible in the near future.





Warren Powell is Professor of Operations Research and Financial Engineering at Princeton University where he has taught since 1981. In 1990, he founded CASTLE Laboratory which spans research in computational stochastic optimization with applications initially in transportation and logistics. In 2011, he founded the Princeton laboratory for ENergy Systems Analysis (PENSA) to tackle the rich array of problems in energy systems analysis. In 2013, this morphed into "CASTLE Labs," focusing on computational stochastic optimization and learning. He developed a method for bridging dynamic programming with math programming to solve very high-dimensional stochastic, dynamic programs using the modeling and algorithmic framework of approximate dynamic programming. This work has been used in a variety of applications including fleet management at Schneider National, the SMART energy resource planning model, and locomotive optimization at Norfolk Southern.


Daniel Jiang is a fourth year PhD student advised by Professor Warren B. Powell in the Department of Operations Research and Financial Engineering. He grew up in West Lafayette, IN and attended Purdue University, where he received B.S. degrees with Highest Distinction in Computer Engineering and Mathematics in 2011. He subsequently received an M.A. in Operations Research and Financial Engineering from Princeton University in 2013. His current research interests are in the area of stochastic optimization, particularly approximate dynamic programming algorithms, with applications in the energy markets.


Intellectual Property Status

Patent protection is pending.

Princeton is seeking to identify appropriate partners for the further development and commercialization of this technology.


Michael Tyerech
Princeton University Office of Technology Licensing • (609) 258-6762•

Laurie Bagley
Princeton University Office of Technology Licensing • (609) 258-5579•


Patent Information:
For Information, Contact:
Chris Wright
Licensing Associate
Princeton University
Warren Powell
Daniel Jiang
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